Abstract:The increased use of disc brakes in passenger cars has led the research world to focus on the prediction of brake performance and wear under different working conditions. A proper model of the brake linings' coefficient of friction (BLCF) is important to monitor the brake operation and increase the performance of control systems such as ABS, TC and ESP by supplying an accurate estimate of the brake torque. The literature of the last decades is replete with semi-empirical and analytical friction models whose derivation comes from significant research that has been conducted into the direction of friction modelling of pin-disc couplings. On the contrary, just a few models have been developed and used for the prediction of the automotive BLCF without obtaining satisfactory results. The present work aims at collecting the current state of art of the estimation techniques for the BLCF, with special attention to the models for automotive brakes. Moreover, the work proposes a classification of the several existing approaches and discusses the relative pro and cons. Finally, based on evidence of the limitations of the model-based approach and the potentialities of the neural networks, the authors propose a new state observer for BLCF estimation as a promising solution among the supporting tools of the control engineering.
The presented paper introduces a new methodology of experimental testing procedures required by the complex systems of electric vehicles (EV). This methodology is based on real-time connection of test setups and platforms, which may be situated in different geographical locations, belong to various cyber-physical domains, and are united in a global X-in-the-loop (XIL) experimental environment. The proposed concept, called XILforEV, allows exploring interdependencies between various physical processes that can be identified or investigated in the process of EV development. The paper discusses the following relevant topics: global XILforEV architecture; realization of required high-confidence models using dynamic data driven application systems (DDDAS) and multi fidelity models (MFM) approaches; and formulation of case studies to illustrate XILforEV applications.
In the presented work a nonlinear, analytic model of a permanent magnet direct current motors with brushes is proposed. Besides the theoretical modeling an automated identification algorithm for this detailed model is deduced. The resulting model includes the electromechanic and electromagnetic effects of the direct current machine, like voltage induction or motor torque, and additional nonlinear phenomena. These nonlinearities include cogging torque, eddy current, hysteresis losses and tribological aspects. The cogging torque is caused by a variation of the magnetic flux density, which manifests itself as a periodic oscillation in the torque curve. In addition, eddy current and hysteresis losses arise by the commutation of the magnetic field in the armature, are also captured by the motor model. The tribological aspects of all friction regimes are modeled utilizing the elasto-plastic friction model. This model can reflect the linear spring damper behavior of the elastic friction domain as well as velocity depending friction behavior of the plastic friction domain. The parameters are separately identified through specific experiments referring to their physical equivalents. Therefore, two testing benches are developed in order to capture the different effects in the direct current motor.
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